Single excitation energies obtained from the ensemble HOMO-LUMO gap: exact results and approximations

In calculations based on density functional theory, the "HOMO-LUMO gap" (difference between the highest occupied and lowest unoccupied molecular orbital energies) is often used as a low-cost, ad hoc approximation for the lowest excitation energy. Here we show that a simple correction based on rigorous ensemble density functional theory makes the HOMO-LUMO gap exact, in principle, and significantly more accurate, in practice. The introduced perturbative ensemble density functional theory approach predicts different and useful values for singlet-singlet and singlet-triplet excitations, using semi-local and hybrid approximations. Excitation energies are of similar quality to time-dependent density functional theory, especially at high fractions of exact exchange. It therefore offers an easy-to-implement and low-cost route to robust prediction of molecular excitation energies.

The Potential of Spectroscopic Techniques in Coffee Analysis—A Review

This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.

Multi-Stage Prediction of Feed System Time Series

In order to reduce the tracking error of the computer numerical control (CNC) feed system and improve the CNC machining accuracy, a novel prediction model is proposed based on fuzzy C-means robust variational echo state network. Firstly, the feed speed time series is clustered, and then reconstructed for different categories. The multi-stage robust prediction models are established to realize the multi-state robust prediction of the CNC machining feed velocity to reduce the tracking error of the feed system. Finally, the reference and actual time series with different feed speed are used to verify the established models. The results show that the proposed method can reduce the tracking error and realize the effective prediction of the time series of the feed system.

Using Machine Learning To Improve the Accuracy of Genomic Prediction on Reproduction Traits in Pigs

Background Recently, machine learning (ML) is becoming attractive in genomic prediction, while its superiority in genomic prediction and the choosing of optimal ML methods are needed investigation. Results In this study, 2566 Chinese Yorkshire pigs with reproduction traits records were used, they were genotyped with GenoBaits Porcine SNP 50K and PorcineSNP50 panel. Four ML methods, including support vector regression (SVR), kernel ridge regression (KRR), random forest (RF) and Adaboost.R2 were implemented. Through 20 replicates of five-fold cross-validation, the genomic prediction abilities of ML methods were explored. Compared with genomic BLUP(GBLUP), single-step GBLUP (ssGBLUP) and Bayesian method BayesHE, our results indicated that ML methods significantly outperformed. The prediction accuracy of ML methods was improved by 19.3%, 15.0% and 20.8% on average over GBLUP, ssGBLUP and BayesHE, ranging from 8.9–24.0%, 7.6–17.5% and 11.1–24.6%, respectively. In addition, ML methods yielded smaller mean squared error (MSE) and mean absolute error (MAE) in all scenarios. ssGBLUP yielded improvement of 3.7% on average compared to GBLUP, and the performance of BayesHE was close to GBLUP. Among four ML methods, SVR and KRR had the most robust prediction abilities, which yielded higher accuracies, lower bias, lower MSE and MAE, and comparable computing efficiency as GBLUP. RF demonstrated the lowest prediction ability and computational efficiency among ML methods. Conclusion Our findings demonstrated that ML methods are more efficient than traditional genomic selection methods, and it could be new options for genomic prediction.

Robust Prediction of Personalized In Vivo Response to Unseen Drugs From In Vitro Screens Using a Novel Context-Aware Deconfounding Autoencoder

Accurate and robust prediction of patient-specific responses to drug treatments is critical for drug development and personalized medicine. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell line data, few of them can reliably predict individual patient clinical responses to new drugs due to data distribution shift and confounding factors. We have developed a novel Context-aware Deconfounding Autoencoder (CODE-AE) that can extract intrinsic biological signals masked by context-specific patterns and confounding factors. Extensive comparative studies demonstrated that CODE-AE effectively alleviated the out-of-distribution problem for the model generalization, significantly improved accuracy and robustness over state-of-the-art methods in predicting patient-specific in vivo drug responses purely from in vitro screens. Using CODE-AE, we screened 59 drugs for 9,808 cancer patients. Our results are consistent with existing clinical observations, suggesting the potential of CODE-AE in developing personalized anti-cancer therapies and drug-response biomarkers.

Maximum Drawdown, Recovery, and Momentum

We empirically test predictability on asset price using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directions of asset prices but also in capturing cross-sectional return differentials. In monthly periods, the alternative portfolios ranked by maximum drawdown measures exhibit outperformance over other alternative momentum portfolios including traditional cumulative return-based momentum portfolios. In weekly time scales, recovery-related stock selection rules are the best ranking criteria for detecting mean-reversion. For the alternative portfolios and their ranking baskets, improved risk profiles in various reward-risk measures also imply more consistent prediction on the direction of assets in future. Moreover, turnover rates of these momentum/contrarian portfolios are also reduced with respect to the benchmark portfolios. In the Carhart four-factor analysis, higher factor-neutral intercepts for the alternative strategies are another evidence for the robust prediction by the alternative stock selection rules.

Flexibility of Microcystis Overwintering Strategy in Response to Winter Temperatures

Microcystis is one of the most common bloom-forming cyanobacteria in freshwater ecosystems throughout the world. However, the underlying life history mechanism and distinct temporal dynamics (inter- and intra-annual) of Microcystis populations in different geographical locations and lakes remain unclear but is critical information needed for the development of robust prediction, prevention, and management strategies. Perennial observations indicate that temperature may be the key factor driving differences in the overwintering strategy. This study quantitatively compared the overwintering abilities of Microcystis aeruginosa (Ma) in both the water column and sediments under a gradient of overwintering water temperatures (i.e., 4, 8, and 12 °C) using the death and proliferation rates of Ma. The results show that the dynamics of the Microcystis overwintering strategy were significantly affected by water temperatures. At 4 and 8 °C, Ma mainly overwintered in sediments and disappeared from the water column after exposure to low temperatures for a long duration, although some Microcystis cells can overwinter in the water column for short durations at low temperatures. At 12 °C, most Ma can overwinter in the water column. Rising temperatures promoted the proliferation of pelagic Ma but accelerated the death of benthic Ma. With warmer winter temperatures, pelagic Microcystis might become the primary inoculum sources in the spring. Our study highlights the overwintering strategy flexibility in explaining temporal dynamics differences of Microcystis among in geographical locations and should be considered in the context of global warming.

Implementation of Landslide Susceptibility Model Using Machine Learning for Semi Detailed Map Scale in Mountainous Region of Java Island

Landslide susceptibility modeling using neural network (ANN) are applied to semi detailed volcanic-sedimentary water catchment. Annually landslide occurred in catchment area frequently in unconsolidated and weathered material combined with uncertainty in rainfall pattern that complicated landslide occurrence. Data used for analysis including landslide inventory, geology, digital elevation related data, distance to stream, and several other available data. Results show that machine learning method yield fair result data based on evaluation on Area under Curve (AUC). Thus, it can be suggested that machine learning methods for landslide susceptibility model could still be develop to produce robust prediction model with different characterization of parameter data and machine learning parameters.

Mixed modulus and anomaly mediation in light of the muon g − 2 anomaly

The new measurement of the anomalous magnetic moment of muon at the Fermilab Muon g− 2 experiment has strengthened the significance of the discrepancy between the standard model prediction and the experimental observation from the BNL measurement. If new physics responsible for the muon g− 2 anomaly is supersymmetric, one should consider how to obtain light electroweakinos and sleptons in a systematic way. The gauge coupling unification allows a robust prediction of the gaugino masses, indicating that the electroweakinos can be much lighter than the gluino if anomaly-mediated supersymmetry breaking is sizable. As naturally leading to mixed modulus-anomaly mediation, the KKLT scenario is of particular interest and is found capable of explaining the muon g− 2 anomaly in the parameter region where the lightest ordinary supersymmetric particle is a bino-like neutralino or slepton.